Overview

Dataset statistics

Number of variables12
Number of observations785456
Missing cells1146981
Missing cells (%)12.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.9 MiB
Average record size in memory96.0 B

Variable types

Numeric6
DateTime3
Text3

Alerts

OwnerDisplayName has 772004 (98.3%) missing valuesMissing
LastEditDate has 374977 (47.7%) missing valuesMissing
Score is highly skewed (γ1 = 32.66621385)Skewed
ViewCount is highly skewed (γ1 = 128.8200658)Skewed
Id has unique valuesUnique
Score has 441895 (56.3%) zerosZeros
AnswerCount has 178726 (22.8%) zerosZeros
CommentCount has 302229 (38.5%) zerosZeros

Reproduction

Analysis started2023-10-15 18:10:13.948286
Analysis finished2023-10-15 18:13:56.807525
Duration3 minutes and 42.86 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Distinct49998
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21354.159
Minimum0
Maximum49997
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2023-10-15T21:13:57.992928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1818
Q19126
median20569
Q332778
95-th percentile43524
Maximum49997
Range49997
Interquartile range (IQR)23652

Descriptive statistics

Standard deviation13517.781
Coefficient of variation (CV)0.633028
Kurtosis-1.163667
Mean21354.159
Median Absolute Deviation (MAD)11777
Skewness0.1718686
Sum1.6772753 × 1010
Variance1.827304 × 108
MonotonicityNot monotonic
2023-10-15T21:13:58.615014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1313 22
 
< 0.1%
4681 22
 
< 0.1%
4691 22
 
< 0.1%
4690 22
 
< 0.1%
4689 22
 
< 0.1%
4688 22
 
< 0.1%
4687 22
 
< 0.1%
4686 22
 
< 0.1%
4685 22
 
< 0.1%
4684 22
 
< 0.1%
Other values (49988) 785236
> 99.9%
ValueCountFrequency (%)
0 21
< 0.1%
1 21
< 0.1%
2 21
< 0.1%
3 21
< 0.1%
4 21
< 0.1%
5 21
< 0.1%
6 21
< 0.1%
7 21
< 0.1%
8 21
< 0.1%
9 21
< 0.1%
ValueCountFrequency (%)
49997 1
< 0.1%
49996 1
< 0.1%
49995 1
< 0.1%
49994 1
< 0.1%
49993 1
< 0.1%
49992 1
< 0.1%
49991 1
< 0.1%
49990 1
< 0.1%
49989 1
< 0.1%
49988 1
< 0.1%

Id
Real number (ℝ)

UNIQUE 

Distinct785456
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71059652
Minimum64627531
Maximum77253066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2023-10-15T21:13:59.238309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum64627531
5-th percentile65285958
Q167899742
median71146176
Q374263733
95-th percentile76625143
Maximum77253066
Range12625535
Interquartile range (IQR)6363991.2

Descriptive statistics

Standard deviation3640996.9
Coefficient of variation (CV)0.051238598
Kurtosis-1.204849
Mean71059652
Median Absolute Deviation (MAD)3174826.5
Skewness-0.049631619
Sum5.581423 × 1013
Variance1.3256859 × 1013
MonotonicityNot monotonic
2023-10-15T21:13:59.893935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77253066 1
 
< 0.1%
68289593 1
 
< 0.1%
68288262 1
 
< 0.1%
68288283 1
 
< 0.1%
68288285 1
 
< 0.1%
68288292 1
 
< 0.1%
68288300 1
 
< 0.1%
68288316 1
 
< 0.1%
68288355 1
 
< 0.1%
68288390 1
 
< 0.1%
Other values (785446) 785446
> 99.9%
ValueCountFrequency (%)
64627531 1
< 0.1%
64627553 1
< 0.1%
64627555 1
< 0.1%
64627576 1
< 0.1%
64627590 1
< 0.1%
64627602 1
< 0.1%
64627612 1
< 0.1%
64627617 1
< 0.1%
64627630 1
< 0.1%
64627639 1
< 0.1%
ValueCountFrequency (%)
77253066 1
< 0.1%
77253065 1
< 0.1%
77253053 1
< 0.1%
77253022 1
< 0.1%
77253020 1
< 0.1%
77253005 1
< 0.1%
77252996 1
< 0.1%
77252992 1
< 0.1%
77252991 1
< 0.1%
77252989 1
< 0.1%
Distinct781082
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Minimum2020-11-01 00:03:26
Maximum2023-10-08 09:37:48
2023-10-15T21:14:00.621591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:14:01.268671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Score
Real number (ℝ)

SKEWED  ZEROS 

Distinct130
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43050661
Minimum-16
Maximum286
Zeros441895
Zeros (%)56.3%
Negative76732
Negative (%)9.8%
Memory size6.0 MiB
2023-10-15T21:14:01.893509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-16
5-th percentile-1
Q10
median0
Q31
95-th percentile2
Maximum286
Range302
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9055843
Coefficient of variation (CV)4.4263765
Kurtosis2684.696
Mean0.43050661
Median Absolute Deviation (MAD)0
Skewness32.666214
Sum338144
Variance3.6312517
MonotonicityNot monotonic
2023-10-15T21:14:02.504592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 441895
56.3%
1 177381
22.6%
2 55080
 
7.0%
-1 50235
 
6.4%
3 18115
 
2.3%
-2 17188
 
2.2%
4 7108
 
0.9%
-3 6279
 
0.8%
5 3257
 
0.4%
-4 2050
 
0.3%
Other values (120) 6868
 
0.9%
ValueCountFrequency (%)
-16 2
 
< 0.1%
-14 1
 
< 0.1%
-13 1
 
< 0.1%
-12 4
 
< 0.1%
-11 1
 
< 0.1%
-10 10
 
< 0.1%
-9 21
 
< 0.1%
-8 39
 
< 0.1%
-7 91
< 0.1%
-6 202
< 0.1%
ValueCountFrequency (%)
286 1
 
< 0.1%
223 1
 
< 0.1%
217 1
 
< 0.1%
197 1
 
< 0.1%
196 1
 
< 0.1%
189 1
 
< 0.1%
186 1
 
< 0.1%
175 1
 
< 0.1%
171 3
< 0.1%
169 1
 
< 0.1%

ViewCount
Real number (ℝ)

SKEWED 

Distinct10886
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean481.79102
Minimum2
Maximum1156519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2023-10-15T21:14:03.108673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile26
Q153
median122
Q3375
95-th percentile1624
Maximum1156519
Range1156517
Interquartile range (IQR)322

Descriptive statistics

Standard deviation2842.4085
Coefficient of variation (CV)5.8996711
Kurtosis39576.622
Mean481.79102
Median Absolute Deviation (MAD)86
Skewness128.82007
Sum3.7842564 × 108
Variance8079286.4
MonotonicityNot monotonic
2023-10-15T21:14:04.035666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 6383
 
0.8%
40 6374
 
0.8%
37 6340
 
0.8%
38 6334
 
0.8%
36 6329
 
0.8%
39 6327
 
0.8%
43 6308
 
0.8%
42 6308
 
0.8%
35 6302
 
0.8%
44 6204
 
0.8%
Other values (10876) 722247
92.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
3 6
 
< 0.1%
4 11
 
< 0.1%
5 38
 
< 0.1%
6 79
 
< 0.1%
7 139
 
< 0.1%
8 220
 
< 0.1%
9 328
< 0.1%
10 482
0.1%
11 642
0.1%
ValueCountFrequency (%)
1156519 1
< 0.1%
567907 1
< 0.1%
433844 1
< 0.1%
352483 1
< 0.1%
343214 1
< 0.1%
324098 1
< 0.1%
319989 1
< 0.1%
253962 1
< 0.1%
236844 1
< 0.1%
235719 1
< 0.1%

AnswerCount
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1092614
Minimum0
Maximum25
Zeros178726
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2023-10-15T21:14:04.904683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile3
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.92280776
Coefficient of variation (CV)0.83191193
Kurtosis9.5366007
Mean1.1092614
Median Absolute Deviation (MAD)0
Skewness1.6887528
Sum871276
Variance0.85157417
MonotonicityNot monotonic
2023-10-15T21:14:05.725216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 419827
53.5%
0 178726
22.8%
2 133175
 
17.0%
3 37986
 
4.8%
4 10774
 
1.4%
5 3320
 
0.4%
6 991
 
0.1%
7 342
 
< 0.1%
8 138
 
< 0.1%
9 65
 
< 0.1%
Other values (14) 112
 
< 0.1%
ValueCountFrequency (%)
0 178726
22.8%
1 419827
53.5%
2 133175
 
17.0%
3 37986
 
4.8%
4 10774
 
1.4%
5 3320
 
0.4%
6 991
 
0.1%
7 342
 
< 0.1%
8 138
 
< 0.1%
9 65
 
< 0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
23 1
 
< 0.1%
22 3
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 3
 
< 0.1%
17 4
< 0.1%
16 1
 
< 0.1%
15 8
< 0.1%
14 5
< 0.1%

Title
Text

Distinct785350
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2023-10-15T21:14:08.102535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length150
Median length124
Mean length61.223578
Min length15

Characters and Unicode

Total characters48088427
Distinct characters424
Distinct categories25 ?
Distinct scripts14 ?
Distinct blocks34 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique785252 ?
Unique (%)> 99.9%

Sample

1st rowHow to solve create next app npm run build placing href with _next instead of .next in the html files from the server?
2nd rowTime series prediction code fails to run in an iterative loop, but works when run out of loop
3rd rowTwo way communication between two Processes python
4th rowPlotting with subplots in a loop in one figure
5th rowAdjust the edge points of a rectangular shaped contour
ValueCountFrequency (%)
to 334970
 
4.2%
in 309066
 
3.8%
a 277256
 
3.4%
how 204769
 
2.5%
python 182503
 
2.3%
the 164140
 
2.0%
of 146085
 
1.8%
with 125800
 
1.6%
and 104765
 
1.3%
from 88303
 
1.1%
Other values (162315) 6128583
76.0%
2023-10-15T21:14:10.862089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7280658
15.1%
e 3891182
 
8.1%
t 3573040
 
7.4%
o 3271964
 
6.8%
n 3162230
 
6.6%
a 2984655
 
6.2%
i 2839037
 
5.9%
r 2393761
 
5.0%
s 2109689
 
4.4%
l 1609555
 
3.3%
Other values (414) 14972656
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37430779
77.8%
Space Separator 7280738
 
15.1%
Uppercase Letter 2046524
 
4.3%
Other Punctuation 714206
 
1.5%
Decimal Number 209307
 
0.4%
Open Punctuation 98027
 
0.2%
Close Punctuation 97254
 
0.2%
Dash Punctuation 96595
 
0.2%
Connector Punctuation 67503
 
0.1%
Math Symbol 28585
 
0.1%
Other values (15) 18909
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3891182
 
10.4%
t 3573040
 
9.5%
o 3271964
 
8.7%
n 3162230
 
8.4%
a 2984655
 
8.0%
i 2839037
 
7.6%
r 2393761
 
6.4%
s 2109689
 
5.6%
l 1609555
 
4.3%
d 1284502
 
3.4%
Other values (136) 10311164
27.5%
Uppercase Letter
ValueCountFrequency (%)
P 234200
 
11.4%
H 198137
 
9.7%
I 168858
 
8.3%
S 152000
 
7.4%
C 133356
 
6.5%
R 132360
 
6.5%
D 110405
 
5.4%
E 99937
 
4.9%
A 97021
 
4.7%
T 95367
 
4.7%
Other values (58) 624883
30.5%
Other Punctuation
ValueCountFrequency (%)
? 191237
26.8%
' 126090
17.7%
. 119840
16.8%
: 96260
13.5%
, 72589
 
10.2%
" 56775
 
7.9%
/ 32747
 
4.6%
* 3446
 
0.5%
\ 3436
 
0.5%
% 3174
 
0.4%
Other values (26) 8612
 
1.2%
Math Symbol
ValueCountFrequency (%)
= 9460
33.1%
+ 7414
25.9%
> 5110
17.9%
< 4160
14.6%
| 1977
 
6.9%
~ 330
 
1.2%
× 31
 
0.1%
21
 
0.1%
18
 
0.1%
11
 
< 0.1%
Other values (19) 53
 
0.2%
Other Symbol
ValueCountFrequency (%)
° 29
37.2%
9
 
11.5%
4
 
5.1%
® 4
 
5.1%
¦ 2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
👩 2
 
2.6%
💻 2
 
2.6%
Other values (18) 20
25.6%
Nonspacing Mark
ValueCountFrequency (%)
̶ 8
22.9%
̃ 3
 
8.6%
3
 
8.6%
̂ 2
 
5.7%
2
 
5.7%
̄ 2
 
5.7%
͡ 2
 
5.7%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (10) 10
28.6%
Other Letter
ValueCountFrequency (%)
ه 9
27.3%
º 5
15.2%
4
12.1%
1
 
3.0%
ǃ 1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
1
 
3.0%
Other values (8) 8
24.2%
Decimal Number
ValueCountFrequency (%)
2 44640
21.3%
0 38859
18.6%
1 38492
18.4%
3 30877
14.8%
4 16869
 
8.1%
5 13212
 
6.3%
6 8874
 
4.2%
8 6792
 
3.2%
9 5586
 
2.7%
7 5097
 
2.4%
Other values (6) 9
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 86858
88.6%
[ 9647
 
9.8%
{ 1488
 
1.5%
15
 
< 0.1%
9
 
< 0.1%
7
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 86434
88.9%
] 9413
 
9.7%
} 1381
 
1.4%
13
 
< 0.1%
9
 
< 0.1%
2
 
< 0.1%
1
 
< 0.1%
1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 1149
96.9%
23
 
1.9%
£ 11
 
0.9%
¢ 1
 
0.1%
1
 
0.1%
¤ 1
 
0.1%
Other Number
ValueCountFrequency (%)
² 19
73.1%
½ 2
 
7.7%
³ 2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 96350
99.7%
142
 
0.1%
90
 
0.1%
12
 
< 0.1%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
7280658
> 99.9%
  75
 
< 0.1%
  4
 
< 0.1%
1
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
` 14383
95.5%
^ 477
 
3.2%
´ 192
 
1.3%
¯ 1
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
820
64.9%
432
34.2%
» 9
 
0.7%
2
 
0.2%
Initial Punctuation
ValueCountFrequency (%)
480
52.3%
434
47.3%
« 4
 
0.4%
Format
ValueCountFrequency (%)
250
96.2%
7
 
2.7%
3
 
1.2%
Spacing Mark
ValueCountFrequency (%)
2
50.0%
ि 1
25.0%
1
25.0%
Modifier Letter
ValueCountFrequency (%)
ˆ 2
66.7%
1
33.3%
Private Use
ValueCountFrequency (%)
􏰁 1
50.0%
􏰂 1
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 67503
100.0%
Control
ValueCountFrequency (%)
45
100.0%
Enclosing Mark
ValueCountFrequency (%)
2
100.0%
Line Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39476958
82.1%
Common 8611082
 
17.9%
Cyrillic 214
 
< 0.1%
Greek 77
 
< 0.1%
Inherited 41
 
< 0.1%
Arabic 19
 
< 0.1%
Devanagari 15
 
< 0.1%
Hebrew 6
 
< 0.1%
Hiragana 4
 
< 0.1%
Han 4
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
7280658
84.5%
? 191237
 
2.2%
' 126090
 
1.5%
. 119840
 
1.4%
- 96350
 
1.1%
: 96260
 
1.1%
( 86858
 
1.0%
) 86434
 
1.0%
, 72589
 
0.8%
_ 67503
 
0.8%
Other values (180) 387263
 
4.5%
Latin
ValueCountFrequency (%)
e 3891182
 
9.9%
t 3573040
 
9.1%
o 3271964
 
8.3%
n 3162230
 
8.0%
a 2984655
 
7.6%
i 2839037
 
7.2%
r 2393761
 
6.1%
s 2109689
 
5.3%
l 1609555
 
4.1%
d 1284502
 
3.3%
Other values (114) 12357343
31.3%
Cyrillic
ValueCountFrequency (%)
С 28
 
13.1%
о 22
 
10.3%
а 13
 
6.1%
в 13
 
6.1%
н 12
 
5.6%
е 12
 
5.6%
к 10
 
4.7%
с 10
 
4.7%
т 10
 
4.7%
и 9
 
4.2%
Other values (22) 75
35.0%
Greek
ValueCountFrequency (%)
λ 10
13.0%
μ 8
10.4%
π 8
10.4%
β 7
 
9.1%
α 7
 
9.1%
θ 6
 
7.8%
σ 4
 
5.2%
δ 3
 
3.9%
Σ 3
 
3.9%
ε 2
 
2.6%
Other values (16) 19
24.7%
Inherited
ValueCountFrequency (%)
̶ 8
19.5%
7
17.1%
3
 
7.3%
̃ 3
 
7.3%
3
 
7.3%
̂ 2
 
4.9%
2
 
4.9%
2
 
4.9%
̄ 2
 
4.9%
͡ 2
 
4.9%
Other values (7) 7
17.1%
Devanagari
ValueCountFrequency (%)
2
13.3%
ि 1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (4) 4
26.7%
Arabic
ValueCountFrequency (%)
ه 9
47.4%
٢ 3
 
15.8%
٠ 2
 
10.5%
١ 1
 
5.3%
٥ 1
 
5.3%
٤ 1
 
5.3%
ۜ 1
 
5.3%
ۣ 1
 
5.3%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%
Thai
ValueCountFrequency (%)
1
50.0%
1
50.0%
Unknown
ValueCountFrequency (%)
􏰁 1
50.0%
􏰂 1
50.0%
Hebrew
ValueCountFrequency (%)
׳ 6
100.0%
Hiragana
ValueCountFrequency (%)
4
100.0%
Georgian
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48083973
> 99.9%
Punctuation 2733
 
< 0.1%
None 1204
 
< 0.1%
Cyrillic 214
 
< 0.1%
Math Operators 64
 
< 0.1%
Math Alphanum 50
 
< 0.1%
Arabic 27
 
< 0.1%
Currency Symbols 24
 
< 0.1%
Arrows 23
 
< 0.1%
Diacriticals 21
 
< 0.1%
Other values (24) 94
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7280658
15.1%
e 3891182
 
8.1%
t 3573040
 
7.4%
o 3271964
 
6.8%
n 3162230
 
6.6%
a 2984655
 
6.2%
i 2839037
 
5.9%
r 2393761
 
5.0%
s 2109689
 
4.4%
l 1609555
 
3.3%
Other values (86) 14968202
31.1%
Punctuation
ValueCountFrequency (%)
820
30.0%
480
17.6%
434
15.9%
432
15.8%
250
 
9.1%
142
 
5.2%
90
 
3.3%
38
 
1.4%
12
 
0.4%
7
 
0.3%
Other values (12) 28
 
1.0%
None
ValueCountFrequency (%)
213
17.7%
´ 192
15.9%
  75
 
6.2%
51
 
4.2%
ı 46
 
3.8%
40
 
3.3%
é 35
 
2.9%
× 31
 
2.6%
° 29
 
2.4%
² 19
 
1.6%
Other values (135) 473
39.3%
Cyrillic
ValueCountFrequency (%)
С 28
 
13.1%
о 22
 
10.3%
а 13
 
6.1%
в 13
 
6.1%
н 12
 
5.6%
е 12
 
5.6%
к 10
 
4.7%
с 10
 
4.7%
т 10
 
4.7%
и 9
 
4.2%
Other values (22) 75
35.0%
Currency Symbols
ValueCountFrequency (%)
23
95.8%
1
 
4.2%
Arrows
ValueCountFrequency (%)
21
91.3%
1
 
4.3%
1
 
4.3%
Math Operators
ValueCountFrequency (%)
18
28.1%
11
17.2%
11
17.2%
7
 
10.9%
3
 
4.7%
3
 
4.7%
2
 
3.1%
2
 
3.1%
2
 
3.1%
1
 
1.6%
Other values (4) 4
 
6.2%
Math Alphanum
ValueCountFrequency (%)
𝑥 10
20.0%
𝑦 4
 
8.0%
𝛽 3
 
6.0%
𝑡 3
 
6.0%
𝑛 2
 
4.0%
𝑠 2
 
4.0%
𝑓 2
 
4.0%
𝔅 1
 
2.0%
𝘯 1
 
2.0%
𝐱 1
 
2.0%
Other values (21) 21
42.0%
Arabic
ValueCountFrequency (%)
ه 9
33.3%
؟ 7
25.9%
٢ 3
 
11.1%
٠ 2
 
7.4%
ُ 1
 
3.7%
١ 1
 
3.7%
٥ 1
 
3.7%
٤ 1
 
3.7%
ۜ 1
 
3.7%
ۣ 1
 
3.7%
Specials
ValueCountFrequency (%)
9
100.0%
Diacriticals
ValueCountFrequency (%)
̶ 8
38.1%
̃ 3
 
14.3%
̂ 2
 
9.5%
̄ 2
 
9.5%
͡ 2
 
9.5%
̥ 1
 
4.8%
̇ 1
 
4.8%
ͤ 1
 
4.8%
͜ 1
 
4.8%
Hebrew
ValueCountFrequency (%)
׳ 6
100.0%
Letterlike Symbols
ValueCountFrequency (%)
5
38.5%
4
30.8%
2
 
15.4%
1
 
7.7%
1
 
7.7%
Hiragana
ValueCountFrequency (%)
4
66.7%
2
33.3%
VS
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Devanagari
ValueCountFrequency (%)
2
13.3%
ि 1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
1
 
6.7%
Other values (4) 4
26.7%
Box Drawing
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Dingbats
ValueCountFrequency (%)
2
40.0%
1
20.0%
1
20.0%
1
20.0%
Geometric Shapes
ValueCountFrequency (%)
2
66.7%
1
33.3%
Katakana
ValueCountFrequency (%)
2
100.0%
Misc Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
Modifier Letters
ValueCountFrequency (%)
ˆ 2
100.0%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%
IPA Ext
ValueCountFrequency (%)
ɛ 1
25.0%
ɑ 1
25.0%
ɔ 1
25.0%
ɷ 1
25.0%
Emoticons
ValueCountFrequency (%)
😐 1
100.0%
Block Elements
ValueCountFrequency (%)
1
100.0%
CJK Compat
ValueCountFrequency (%)
1
100.0%
Phonetic Ext
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Georgian
ValueCountFrequency (%)
1
100.0%
Thai
ValueCountFrequency (%)
1
50.0%
1
50.0%
Alphabetic PF
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Diacriticals Sup
ValueCountFrequency (%)
᷿ 1
100.0%
Misc Technical
ValueCountFrequency (%)
1
100.0%

Tags
Text

Distinct300067
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
2023-10-15T21:14:12.145911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length114
Median length90
Mean length30.515018
Min length3

Characters and Unicode

Total characters23968204
Distinct characters42
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique253160 ?
Unique (%)32.2%

Sample

1st row<python><reactjs><django><next.js>
2nd row<python><deep-learning><time-series>
3rd row<python><tkinter><multiprocessing>
4th row<python><matplotlib>
5th row<python><opencv><computer-vision><mask><contour>
ValueCountFrequency (%)
python 38457
 
4.9%
python><pandas 19148
 
2.4%
r 18029
 
2.3%
python><pandas><dataframe 14100
 
1.8%
python><python-3.x 8288
 
1.1%
python><django 6559
 
0.8%
r><ggplot2 5739
 
0.7%
python><tkinter 4563
 
0.6%
r><dplyr 3967
 
0.5%
python><numpy 3784
 
0.5%
Other values (300053) 662822
84.4%
2023-10-15T21:14:16.641156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
< 2507927
 
10.5%
> 2507927
 
10.5%
t 1767767
 
7.4%
n 1669243
 
7.0%
o 1663103
 
6.9%
p 1438458
 
6.0%
a 1356639
 
5.7%
e 1187715
 
5.0%
y 1070383
 
4.5%
r 1059924
 
4.4%
Other values (32) 7739118
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18215210
76.0%
Math Symbol 5020723
 
20.9%
Dash Punctuation 515500
 
2.2%
Decimal Number 131870
 
0.6%
Other Punctuation 84901
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1767767
 
9.7%
n 1669243
 
9.2%
o 1663103
 
9.1%
p 1438458
 
7.9%
a 1356639
 
7.4%
e 1187715
 
6.5%
y 1070383
 
5.9%
r 1059924
 
5.8%
h 937994
 
5.1%
s 932625
 
5.1%
Other values (16) 5131359
28.2%
Decimal Number
ValueCountFrequency (%)
3 70789
53.7%
2 31398
23.8%
5 6969
 
5.3%
1 4702
 
3.6%
0 4661
 
3.5%
4 4414
 
3.3%
7 3007
 
2.3%
6 2809
 
2.1%
8 2176
 
1.7%
9 945
 
0.7%
Math Symbol
ValueCountFrequency (%)
< 2507927
50.0%
> 2507927
50.0%
+ 4869
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 84107
99.1%
# 794
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 515500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18215210
76.0%
Common 5752994
 
24.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1767767
 
9.7%
n 1669243
 
9.2%
o 1663103
 
9.1%
p 1438458
 
7.9%
a 1356639
 
7.4%
e 1187715
 
6.5%
y 1070383
 
5.9%
r 1059924
 
5.8%
h 937994
 
5.1%
s 932625
 
5.1%
Other values (16) 5131359
28.2%
Common
ValueCountFrequency (%)
< 2507927
43.6%
> 2507927
43.6%
- 515500
 
9.0%
. 84107
 
1.5%
3 70789
 
1.2%
2 31398
 
0.5%
5 6969
 
0.1%
+ 4869
 
0.1%
1 4702
 
0.1%
0 4661
 
0.1%
Other values (6) 14145
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23968204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
< 2507927
 
10.5%
> 2507927
 
10.5%
t 1767767
 
7.4%
n 1669243
 
7.0%
o 1663103
 
6.9%
p 1438458
 
6.0%
a 1356639
 
5.7%
e 1187715
 
5.0%
y 1070383
 
4.5%
r 1059924
 
4.4%
Other values (32) 7739118
32.3%

CommentCount
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9042862
Minimum0
Maximum62
Zeros302229
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size6.0 MiB
2023-10-15T21:14:17.214013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum62
Range62
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.431166
Coefficient of variation (CV)1.276681
Kurtosis10.163676
Mean1.9042862
Median Absolute Deviation (MAD)1
Skewness2.3039242
Sum1495733
Variance5.9105682
MonotonicityNot monotonic
2023-10-15T21:14:18.052042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 302229
38.5%
2 128241
16.3%
1 128029
16.3%
3 79190
 
10.1%
4 53287
 
6.8%
5 32806
 
4.2%
6 21466
 
2.7%
7 13375
 
1.7%
8 8966
 
1.1%
9 5724
 
0.7%
Other values (35) 12143
 
1.5%
ValueCountFrequency (%)
0 302229
38.5%
1 128029
16.3%
2 128241
16.3%
3 79190
 
10.1%
4 53287
 
6.8%
5 32806
 
4.2%
6 21466
 
2.7%
7 13375
 
1.7%
8 8966
 
1.1%
9 5724
 
0.7%
ValueCountFrequency (%)
62 1
< 0.1%
61 1
< 0.1%
46 1
< 0.1%
44 1
< 0.1%
43 1
< 0.1%
42 2
< 0.1%
39 1
< 0.1%
38 2
< 0.1%
37 1
< 0.1%
36 1
< 0.1%

OwnerDisplayName
Text

MISSING 

Distinct5217
Distinct (%)38.8%
Missing772004
Missing (%)98.3%
Memory size6.0 MiB
2023-10-15T21:14:19.810392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length24
Median length12
Mean length11.814451
Min length3

Characters and Unicode

Total characters158928
Distinct characters93
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2945 ?
Unique (%)21.9%

Sample

1st rowJoe
2nd rownot_a_generic_user
3rd rowuser20775003
4th rowKorodoro
5th rowDan Swogger
ValueCountFrequency (%)
user16774617 80
 
0.6%
user12314098 73
 
0.5%
user2110417 71
 
0.5%
anon 52
 
0.4%
user18948933 50
 
0.4%
user13467695 49
 
0.4%
user18313765 44
 
0.3%
user17582908 39
 
0.3%
user7864386 39
 
0.3%
user14251114 37
 
0.3%
Other values (5321) 13061
96.1%
2023-10-15T21:14:22.215088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19462
12.2%
e 13288
 
8.4%
r 13225
 
8.3%
s 13192
 
8.3%
u 13119
 
8.3%
4 10210
 
6.4%
5 10026
 
6.3%
6 9628
 
6.1%
2 9430
 
5.9%
7 9301
 
5.9%
Other values (83) 38047
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103432
65.1%
Lowercase Letter 54878
34.5%
Uppercase Letter 439
 
0.3%
Space Separator 143
 
0.1%
Other Punctuation 17
 
< 0.1%
Connector Punctuation 14
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13288
24.2%
r 13225
24.1%
s 13192
24.0%
u 13119
23.9%
a 369
 
0.7%
n 311
 
0.6%
o 222
 
0.4%
i 215
 
0.4%
l 137
 
0.2%
t 118
 
0.2%
Other values (39) 682
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
S 44
 
10.0%
A 41
 
9.3%
M 33
 
7.5%
D 32
 
7.3%
C 28
 
6.4%
P 27
 
6.2%
J 24
 
5.5%
R 23
 
5.2%
B 22
 
5.0%
H 20
 
4.6%
Other values (19) 145
33.0%
Decimal Number
ValueCountFrequency (%)
1 19462
18.8%
4 10210
9.9%
5 10026
9.7%
6 9628
9.3%
2 9430
9.1%
7 9301
9.0%
9 9169
8.9%
3 8871
8.6%
8 8805
8.5%
0 8530
8.2%
Other Punctuation
ValueCountFrequency (%)
. 15
88.2%
' 2
 
11.8%
Space Separator
ValueCountFrequency (%)
143
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103611
65.2%
Latin 55290
34.8%
Cyrillic 16
 
< 0.1%
Greek 11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13288
24.0%
r 13225
23.9%
s 13192
23.9%
u 13119
23.7%
a 369
 
0.7%
n 311
 
0.6%
o 222
 
0.4%
i 215
 
0.4%
l 137
 
0.2%
t 118
 
0.2%
Other values (46) 1094
 
2.0%
Common
ValueCountFrequency (%)
1 19462
18.8%
4 10210
9.9%
5 10026
9.7%
6 9628
9.3%
2 9430
9.1%
7 9301
9.0%
9 9169
8.8%
3 8871
8.6%
8 8805
8.5%
0 8530
8.2%
Other values (5) 179
 
0.2%
Cyrillic
ValueCountFrequency (%)
и 2
12.5%
е 2
12.5%
р 2
12.5%
л 1
 
6.2%
б 1
 
6.2%
К 1
 
6.2%
й 1
 
6.2%
т 1
 
6.2%
н 1
 
6.2%
в 1
 
6.2%
Other values (3) 3
18.8%
Greek
ValueCountFrequency (%)
α 2
18.2%
ς 2
18.2%
δ 1
9.1%
ύ 1
9.1%
ο 1
9.1%
Κ 1
9.1%
τ 1
9.1%
σ 1
9.1%
ώ 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158897
> 99.9%
Cyrillic 16
 
< 0.1%
None 15
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19462
12.2%
e 13288
 
8.4%
r 13225
 
8.3%
s 13192
 
8.3%
u 13119
 
8.3%
4 10210
 
6.4%
5 10026
 
6.3%
6 9628
 
6.1%
2 9430
 
5.9%
7 9301
 
5.9%
Other values (57) 38016
23.9%
Cyrillic
ValueCountFrequency (%)
и 2
12.5%
е 2
12.5%
р 2
12.5%
л 1
 
6.2%
б 1
 
6.2%
К 1
 
6.2%
й 1
 
6.2%
т 1
 
6.2%
н 1
 
6.2%
в 1
 
6.2%
Other values (3) 3
18.8%
None
ValueCountFrequency (%)
α 2
13.3%
ς 2
13.3%
é 1
 
6.7%
á 1
 
6.7%
δ 1
 
6.7%
ύ 1
 
6.7%
ο 1
 
6.7%
Κ 1
 
6.7%
í 1
 
6.7%
ö 1
 
6.7%
Other values (3) 3
20.0%

LastEditDate
Date

MISSING 

Distinct409200
Distinct (%)99.7%
Missing374977
Missing (%)47.7%
Memory size6.0 MiB
Minimum2020-11-01 00:27:27
Maximum2023-10-08 09:18:34
2023-10-15T21:14:23.813344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:14:24.784635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct781582
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size6.0 MiB
Minimum2020-11-01 00:03:26
Maximum2023-10-08 09:40:17
2023-10-15T21:14:25.925219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:14:27.076081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-10-15T21:13:41.363036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:05.391972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:18.642353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:26.224420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:32.047142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:37.000523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:42.095653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:10.328456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:20.049876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:27.428479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:32.995463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:37.703837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:43.011218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:11.497264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:22.162071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:28.292567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:33.934947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:38.421666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:43.888468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:12.662802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:23.281087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:29.212110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:34.698247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:39.178730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:44.657509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:14.428393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:24.309822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:30.153510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:35.444205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:39.916867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:45.362190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:16.656501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:25.181313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:31.015439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:36.186115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T21:13:40.624749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-15T21:14:27.995476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0IdScoreViewCountAnswerCountCommentCount
Unnamed: 01.000-0.010-0.0180.030-0.0000.013
Id-0.0101.000-0.099-0.369-0.153-0.032
Score-0.018-0.0991.0000.2470.075-0.121
ViewCount0.030-0.3690.2471.0000.2330.013
AnswerCount-0.000-0.1530.0750.2331.000-0.149
CommentCount0.013-0.032-0.1210.013-0.1491.000

Missing values

2023-10-15T21:13:46.369993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-15T21:13:48.483513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-10-15T21:13:52.613578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0IdCreationDateScoreViewCountAnswerCountTitleTagsCommentCountOwnerDisplayNameLastEditDateLastActivityDate
047323772530662023-10-08 09:37:48050How to solve create next app npm run build placing href with _next instead of .next in the html files from the server?<python><reactjs><django><next.js>0NaNNaN2023-10-08 09:37:48
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247321772530532023-10-08 09:33:04080Two way communication between two Processes python<python><tkinter><multiprocessing>0NaNNaN2023-10-08 09:33:04
347320772530222023-10-08 09:23:000101Plotting with subplots in a loop in one figure<python><matplotlib>0NaNNaN2023-10-08 09:37:31
447319772530202023-10-08 09:22:32060Adjust the edge points of a rectangular shaped contour<python><opencv><computer-vision><mask><contour>0NaNNaN2023-10-08 09:22:32
547318772530052023-10-08 09:17:09050builtins.AttributeError: 'sqlite3.Connection' object has no attribute 'enable_load_extension'<python><python-3.x><sqlite><python-3.6><python-venv>0NaNNaN2023-10-08 09:17:09
647317772529912023-10-08 09:13:06070Sending `multipart/form-data` to AWS Lambda emulator locally<python><amazon-web-services><docker><aws-lambda><multipartform-data>0NaN2023-10-08 09:18:292023-10-08 09:18:29
747316772529892023-10-08 09:12:33070I used Dask to read my 7gb CSV but now there is an error<python><dask>1NaNNaN2023-10-08 09:12:33
847315772529672023-10-08 09:04:47-170can i use trapezium bounding boxing in yolov7 instead of rectangle bonding boxes<python><computer-vision><object-detection><yolov7>0NaNNaN2023-10-08 09:04:47
947314772529522023-10-08 09:00:53-290How to copy a table and insert it behind the original table?<python><docx>0NaNNaN2023-10-08 09:00:53
Unnamed: 0IdCreationDateScoreViewCountAnswerCountTitleTagsCommentCountOwnerDisplayNameLastEditDateLastActivityDate
78544643764731860672022-07-31 18:48:4523282Change colour of geom_point for maximum y value<r><ggplot2><data-visualization><geom-point>2NaN2022-07-31 19:12:382022-07-31 19:12:38
78544743765731860872022-07-31 18:50:310620R: Propensity score with matchit package for data with variables specific to subgroups<r><propensity-score-matching>2NaNNaN2023-06-11 15:00:25
78544843766731860912022-07-31 18:50:4622721Connecting R Shiny to R Script with Function<r><shiny>6NaN2022-08-01 02:45:162022-08-01 02:56:16
78544943767731871232022-07-31 21:45:260681How to get _FILE_ macro enabled using Rcpp?<c++><r><file><imagemagick><rcpp>7NaN2023-03-10 21:47:432023-03-10 21:47:43
78545043768731873302022-07-31 22:24:3201371Not automatically downloading dropbox files when opening data in R<r><synchronization><dropbox>2NaN2022-07-31 22:54:072022-08-01 21:03:44
78545143769731873392022-07-31 22:26:0344621Can I apply styling to inline code in an R Notebook?<r><r-markdown><rnotebook><inline-code>1NaN2022-08-02 21:44:112022-08-02 21:44:11
78545243770731874262022-07-31 22:45:591251Windows Rstudio 4.1.2 and neo4r_0.1.1 connection problem<r><neo4r>0NaNNaN2022-08-02 01:08:55
78545343791731867662022-07-31 20:40:4801392Filter rows that are matched on multiple conditions<r><dataframe><dplyr><data.table><stringr>0NaN2022-07-31 22:34:412022-07-31 23:15:08
78545443792731867862022-07-31 20:46:1211993Sample Multiple Columns Without Repeats R dplyr<r><random><dplyr><probability><sample>1NaNNaN2022-07-31 23:57:26
78545543864733276342022-07-27 19:56:281162Comparing function to variable in R<r><dplyr>0EisenNaN2022-08-11 22:59:05